ArrayMorph is a software to manage array data stored on cloud object storage efficiently. It supports both HDF5 C++ API and h5py API. The data returned by h5py API is numpy arrays. By using h5py API, users can access array data stored on the cloud and feed the read data into machine learning pipelines seamlessly.
Explanation for ArrayMorph. ArrayMorph is a software to manage array data stored on cloud object storage efficiently.
How-To Guides for ArrayMorph. ArrayMorph is a software to manage array data stored on cloud object storage efficiently.
Tutorials for ArrayMorph. ArrayMorph is a software to manage array data stored on cloud object storage efficiently. It supports both HDF5 C++ API and h5py API.
Explanation for Camera Traps Edge Software. The Camera Traps application is both a simulator and IoT device software for utilizing machine learning on the…
How-To Guide for Camera Traps Edge Software. The Camera Traps application is both a simulator and IoT device software for utilizing machine learning on the…
The Camera Traps application is both a simulator and IoT device software for utilizing machine learning on the edge in field research. The first implementation specializes in applying computer vision (detection and classification) to wildlife images for animal ecology studies. Two operational modes are supported (1) an input dataset of images to act as the images that would be generated an IoT camera device or (2) an input video file that would be captured by a camera which is then processed by an image detecting plugin that saves frames with motion in them; it uses these images to drive the simulation.
The ctcontroller tool can be used to manage the provisioning and releasing of edge hardware as well as running and shutting down the camera-traps application.
Explanation for CT-Controller. The ctcontroller tool can be used to manage the provisioning and releasing of edge hardware as well as running and shutting…
How-To Guide for CT-Controller. The ctcontroller tool can be used to manage the provisioning and releasing of edge hardware as well as running and shutting…
GitHub Repo
Explanation for Cyberinfrastructure Knowledge Network. The Cyberinfrastructure Knowledge Network (CKN) is an extensible and portable distributed framework…
How-To Guides for Cyberinfrastructure Knowledge Network. The Cyberinfrastructure Knowledge Network (CKN) is an extensible and portable distributed framework…
Tutorials for Cyberinfrastructure Knowledge Network. The Cyberinfrastructure Knowledge Network (CKN) is an extensible and portable distributed framework…
A browser-based geospatial data discovery and collection interface for selecting an area of interest, checking satellite scene availability, configuring collection parameters, and submitting GeoHarmonizer collection jobs to HPC resources through Tapis.
Explanation for Earth Data Hub. A browser-based geospatial data discovery and collection interface for selecting an area of interest, checking satellite scene…
How-To Guides for Earth Data Hub. A browser-based geospatial data discovery and collection interface for selecting an area of interest, checking satellite…
Tutorials for Earth Data Hub. A browser-based geospatial data discovery and collection interface for selecting an area of interest, checking satellite scene…
FastKG is a knowledge graph embedding training library. Knowledge Graph (KG) embeddings are a way to represent entities and relationships from a KG in a continuous vector space, enabling tasks like link prediction and reasoning. TransE, a popular model, represents relationships as translations in the embedding space, such that for a valid triplet (head, relation, tail), the embedding of the head plus the relation vector is close to the embedding of the tail. Training data for TransE is typically stored in a tab-separated values (TSV) format, where each line represents a triplet, e.g., entity1\trelation1\tentity2. For example a dummy train.tsv should look like this:
How-To Guides for FastKG. FastKG is a knowledge graph embedding training library. Knowledge Graph (KG) embeddings are a way to represent entities and…
Overview
How-To Guide for HARP. Researchers use high-performance computing (HPC) cyberinfrastructures (CI) like the Ohio Supercomputer (OSC) or Texas Advanced…
FastAPI service that turns text into embedding vectors using Qwen3-Embedding-0.6B (GGUF quantized) via llama-cpp-python, designed for the ICICLE AI Tapis tenant. The service runs the model locally — no external API calls — so a single .gguf file plus a Tapis token is everything a deployment needs.
FastAPI + Qdrant vector storage and retrieval service for the ICICLE AI Tapis tenant. Clients provide their own pre-computed embeddings — the service handles storage, filtered search, and reranking.
Explanation for ICICLE Embedding Service. FastAPI service that turns text into embedding vectors using Qwen3-Embedding-0.6B (GGUF quantized) via…
How-To Guides for ICICLE Embedding Service. FastAPI service that turns text into embedding vectors using Qwen3-Embedding-0.6B (GGUF quantized) via…
Tutorials for ICICLE Embedding Service. FastAPI service that turns text into embedding vectors using Qwen3-Embedding-0.6B (GGUF quantized) via…
Explanation for ICICLE Vector DB Service. FastAPI + Qdrant vector storage and retrieval service for the ICICLE AI Tapis tenant.
How-To Guides for ICICLE Vector DB Service. FastAPI + Qdrant vector storage and retrieval service for the ICICLE AI Tapis tenant.
Tutorials for ICICLE Vector DB Service. FastAPI + Qdrant vector storage and retrieval service for the ICICLE AI Tapis tenant.
Ilúvatar is an open Serverless platform built with the goal of jumpstarting and streamlining FaaS research.
Explanation for Ilúvatar. Ilúvatar is an open Serverless platform built with the goal of jumpstarting and streamlining FaaS research.
How-To Guides for Ilúvatar. Ilúvatar is an open Serverless platform built with the goal of jumpstarting and streamlining FaaS research.
The Intelligent Edge Management Service (IEMS) is ICICLE's fleet control plane for deploying and operating AI workloads on edge devices. It provides a browser dashboard and REST API to enroll devices, publish model cards, deploy containers to Jetsons and similar hardware, monitor heartbeats, and manage live camera streams. The service integrates with Tapis for authentication and hosting, Patra for model provenance, and the ICICLE TapisUI extension for portal access.
Explanation for Intelligent Edge Management. The Intelligent Edge Management Service (IEMS) is ICICLE's fleet control plane for deploying and operating AI…
How-To Guides for Intelligent Edge Management. The Intelligent Edge Management Service (IEMS) is ICICLE's fleet control plane for deploying and operating AI…
Tutorials for Intelligent Edge Management. The Intelligent Edge Management Service (IEMS) is ICICLE's fleet control plane for deploying and operating AI…
iSpLib is an accelerated sparse kernel library with PyTorch interface. This library has an auto-tuner which generates optimized custom sparse kernels based on the user environment. The goal of this library is to provide efficient sparse operations for Graph Neural Network implementations. Currently it has support for CPU-based efficient Sparse Dense Matrix Multiplication (spmm-sum only) with autograd.
Explanation for iSpLib. iSpLib is an accelerated sparse kernel library with PyTorch interface. This library has an auto-tuner which generates optimized custom…
How-To Guide for iSpLib. iSpLib is an accelerated sparse kernel library with PyTorch interface. This library has an auto-tuner which generates optimized…
Tutorial for iSpLib. iSpLib is an accelerated sparse kernel library with PyTorch interface. This library has an auto-tuner which generates optimized custom…
The ML Field Planner is a framework for analyzing ML pipelines and studying edge-to-center tradeoffs regarding function placement of ML. Using ML Field Planner, researchers configure experiments to run on real IoT hardware, configure machine learning models to analyze custom benchmark datasets, and experiment with different algorithm configurations, such as storage compression, all from a graphical user interface.
Explanation for ML Field Planner. The ML Field Planner is a framework for analyzing ML pipelines and studying edge-to-center tradeoffs regarding function…
How-To Guide for ML Field Planner. The ML Field Planner is a framework for analyzing ML pipelines and studying edge-to-center tradeoffs regarding function…
A browser-based OpenCV pipeline builder: build an image pre-processing
Explanation for No-Code Image Lab. A browser-based OpenCV pipeline builder: build an image pre-processing pipeline in an interactive editor with live preview…
How-To Guides for No-Code Image Lab. A browser-based OpenCV pipeline builder: build an image pre-processing pipeline in an interactive editor with live…
Tutorials for No-Code Image Lab. A browser-based OpenCV pipeline builder: build an image pre-processing pipeline in an interactive editor with live preview…
A browser-based workflow builder for the full ML lifecycle: wire up a pipeline in
Explanation for No-Code Workflow Studio. A browser-based workflow builder for the full ML lifecycle: wire up a pipeline in a drag-and-drop canvas — data…
How-To Guides for No-Code Workflow Studio. A browser-based workflow builder for the full ML lifecycle: wire up a pipeline in a drag-and-drop canvas — data…
Tutorials for No-Code Workflow Studio. A browser-based workflow builder for the full ML lifecycle: wire up a pipeline in a drag-and-drop canvas — data…
GitHub Repo
Explanation for Patra AI Cards Toolkit. The Patra Toolkit is a component of the Patra AI Cards framework designed to simplify the process of creating and…
How-To Guides for Patra AI Cards Toolkit. The Patra Toolkit is a component of the Patra AI Cards framework designed to simplify the process of creating and…
Tutorials for Patra AI Cards Toolkit. The Patra Toolkit is a component of the Patra AI Cards framework designed to simplify the process of creating and…
Vue 3 + Vite web interface for the Patra Knowledge Base — the frontend component of the Patra AI Cards framework. Browse model cards and datasheets, submit and edit records, and explore edge-deployment experiments.
Explanation for Patra Frontend. Vue 3 + Vite web interface for the Patra Knowledge Base — the frontend component of the Patra AI Cards framework.
How-To Guides for Patra Frontend. Vue 3 + Vite web interface for the Patra Knowledge Base — the frontend component of the Patra AI Cards framework.
# Patra Knowledge Base
Explanation for Patra Knowledge Base. The Patra Knowledge Base is a system designed for managing AI model cards, datasheets, and agent cards throughout the…
How-To Guides for Patra Knowledge Base. The Patra Knowledge Base is a system designed for managing AI model cards, datasheets, and agent cards throughout the…
Tutorial for Patra Knowledge Base. The Patra Knowledge Base is a system designed for managing AI model cards, datasheets, and agent cards throughout the life…
Explanation for Profiling Compiler. Cetus Source to Source compiler improvements are being done at the University of Delaware.
How-To Guides for Profiling Compiler. Cetus Source to Source compiler improvements are being done at the University of Delaware.
This project introduces an agentic approach for high-level and multi-purpose compilers
Explanation for Smart Compiler. This project introduces an agentic approach for high-level and multi-purpose compilers
How-To Guides for Smart Compiler. This project introduces an agentic approach for high-level and multi-purpose compilers
Tutorials for Smart Compiler. This project introduces an agentic approach for high-level and multi-purpose compilers
An AI-driven educational framework that integrates automated curriculum generation with an end-to-end computer vision pipeline, enabling learning for high school and undergraduate students combining domain agnostic datasets with machine learning and AI concepts.
Tutorials for Smart Curriculum Designer. An AI-driven educational framework that integrates automated curriculum generation with an end-to-end computer vision…
A 7-step HPC-backed pipeline for few-shot object detection — from interactive image annotation through class support generation, proposal visualization, and Tapis-powered job execution — built to work across any research domain. Also includes a dedicated Semantic Segmentation pipeline for polygon and pixel-mask annotation with SAM3-assisted labeling.
Explanation for Smart Labeling Service for Object Detection. A 7-step HPC-backed pipeline for few-shot object detection — from interactive image annotation…
How-To Guides for Smart Labeling Service for Object Detection. A 7-step HPC-backed pipeline for few-shot object detection — from interactive image annotation…
Tutorials for Smart Labeling Service for Object Detection. A 7-step HPC-backed pipeline for few-shot object detection — from interactive image annotation…
Cetus Source to Source compiler improvements are being done at the University of Delaware. In this release, we release cetus base plus an static profiling feature and the instructions to use it.